




'''simple docstring'''


import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


UpperCAmelCase_					=   get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
  UpperCAmelCase_					=   json.load(f)





@require_torch
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     int:
     return FSMTTokenizer.from_pretrained(UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     int:
     __a								=       FSMTForConditionalGeneration.from_pretrained(UpperCamelCase   ).to(UpperCamelCase   )
     if torch_device == "cuda":
       model.half()
     return model







   @parameterized.expand(
       [
           ['en-ru', 26.0],
           ['ru-en', 22.0],
           ['en-de', 22.0],
           ['de-en', 29.0],
       ]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     int:
     # note: this test is not testing the best performance since it only evals a small batch
     # but it should be enough to detect a regression in the output quality
     __a								=       f"facebook/wmt19-{pair}"
     __a								=       self.get_tokenizer(UpperCamelCase   )
     __a								=       self.get_model(UpperCamelCase   )

     __a								=       bleu_data[pair]['src']
     __a								=       bleu_data[pair]['tgt']

     __a								=       tokenizer(UpperCamelCase   ,				return_tensors='pt'   ,				truncation=UpperCamelCase   ,				padding='longest'   ).to(UpperCamelCase   )
     __a								=       model.generate(
         input_ids=batch.input_ids   ,				num_beams=8   ,				)
     __a								=       tokenizer.batch_decode(
         UpperCamelCase   ,				skip_special_tokens=UpperCamelCase   ,				clean_up_tokenization_spaces=UpperCamelCase   )
     __a								=       calculate_bleu(UpperCamelCase   ,				UpperCamelCase   )
     print(UpperCamelCase   )
     self.assertGreaterEqual(scores['bleu']   ,				UpperCamelCase   )









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ClapModel",
      "ClapPreTrainedModel",
      "ClapTextModel",
      "ClapTextModelWithProjection",
      "ClapAudioModel",
      "ClapAudioModelWithProjection",
  ]
  UpperCAmelCase_					=   ["ClapFeatureExtractor"]

if TYPE_CHECKING:
  from .configuration_clap import (
      CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
      ClapAudioConfig,
      ClapConfig,
      ClapTextConfig,
  )
  from .processing_clap import ClapProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_clap import ClapFeatureExtractor
    from .modeling_clap import (
        CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
        ClapAudioModel,
        ClapAudioModelWithProjection,
        ClapModel,
        ClapPreTrainedModel,
        ClapTextModel,
        ClapTextModelWithProjection,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



